\begin{table}

\caption{\label{tab:performances}Performance of models trained with different approaches and alorithms. Models sorted by macro F1 score within approach.}
\centering
\fontsize{9}{11}\selectfont
\begin{tabular}[t]{llccccc}
\toprule
 & Model & $F1_{\mbox{macro}}$ & $F1_{\mbox{micro}}$ & Precision & Recall & Specificity\\
\midrule
\addlinespace[0.3em]
\multicolumn{7}{l}{\textbf{LLM fine-tuning}}\\
\hspace{1em} & XLM-Twitter & 0.745 & 0.815 & 0.644 & 0.581 & 0.893\\

\addlinespace[0.3em]
\multicolumn{7}{l}{\textbf{MSE}}\\
\hspace{1em} & XLM-R + Ridge regression & 0.685 & 0.737 & 0.482 & 0.660 & 0.763\\

\hspace{1em} & XLM-R + MLP & 0.639 & 0.777 & 0.604 & 0.317 & 0.931\\

\hspace{1em} & LASER + Ridge regression & 0.643 & 0.688 & 0.422 & 0.664 & 0.696\\

\hspace{1em} & LASER + MLP & 0.630 & 0.765 & 0.552 & 0.321 & 0.913\\

\addlinespace[0.3em]
\multicolumn{7}{l}{\textbf{MT\+BoW}}\\
\hspace{1em} & Ridge regression & 0.611 & 0.674 & 0.391 & 0.543 & 0.718\\

\hspace{1em} & SVM & 0.585 & 0.704 & 0.394 & 0.336 & 0.827\\

\hspace{1em} & Naive Bayes & 0.574 & 0.664 & 0.352 & 0.408 & 0.749\\

\hspace{1em} & MLP & 0.428 & 0.750 & 0.000 & 0.000 & 1.000\\
\bottomrule
\end{tabular}
\end{table}
